Some of the issues you may wish to discuss include:The first 2 and the last author (ie. the most significant ones) have all received payment from the company making PCT testing. Does that just mean they are interested in the topic? Is it unreasonable to ask for no conflict of interest?Our patients are part of this population but not huge in number. Does that make the results irrelevant to us, or is there a lot we can still learn?What do the results tell us about how we should use PCT? Do we do that currently?What do you think of the blinding strategy - adequate?The study had antibiotic use as it's focus, not patient outcome. The benefits are therefore to the population rather than the individual patient. Do you agree? The protocol was overruled in 1/5 of patients. Is this a reflection on the study design the test, or the physicians involved?

It is a non-inferiority trial performed on non-ICU patients with LRTI. The primary outcome was composite of overall adverse outcomes. If you calculate your sample size based on this primary outcome, can you still use the same sample size towards your secondary outcome measures (e.g. antibiotic exposure - a superiority end point)? How accurate would that be? What is a non-inferiority trial?

If you have a few patients withdrawing consent after randomisation and if you remove them from final analysis, would you still call that an intention to treat analysis? In this paper they did not include such patients, but it doesn't really matter. Why do I say it doesn't matter? What other type of analysis do you know? Why do we care about intention to treat analysis?

PCT use leads to a reduction of antibiotic exposure by 3 days. Is this effect large or small? Why is it important?

If PCT is such a good measure, why is it struggling to get into routine clinical practice?

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Emma

8/11/2013 04:36:11 am

Few thoughts..
Without getting into detail about the actual study design/stats I think use of procalcitonin seems only to be a good thing, and seemingly not inferior to blanket antiobiotic prescribing to patients presenting with a 'LRTI'- for a whole host of reasons. I think the benefits of PCT apply to the patient as well as the population as the side effect rates dropped substanitially, and N+V although seemingly minor side effect is pretty awful for the patient. I dont really understand why Sunderland is the only unit that utilises PCT in appraoch to risk stratification/management- is there a hidden problem with it? how much does it cost? Does this cost outweigh cost saving from sensible use of antibiotics? Also where does PCT actually come from - i understand that if given to a patient it would actually cause harmful effects and that levels correlate with degree of 'badness'- is it produced in the thyroid follicular cells?

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Deva

8/11/2013 08:08:34 am

Emma
The parafollicular C cells of the thyroid secrete PCT. It is a prohormone for calcitonin, which plays a (minor) role in calcium homeostasis (anti-PTH). Although most cells have the PCT secreting gene, they are normally in a suppressed state. In sepsis, the suppression is lifted and a large amount of PCT is produced as a response (thyroidectomised septic patients mount a similar PCT response). Liver, lungs and intestines have all been proposed as possible sites but the majority of PCT comes from adipose tissue. Unlike thyroid, these cells lack the processing granules; therefore, PCT does not get cleaved into calcitonin and secreted as such into blood stream.

The average cost is £12-15 per test (includes all overhead costs). Not sure whether SRH has a better deal. My senior colleagues will know better.

Finally think about what you normally do in your daily practice. Do you prescribe antibiotics for this long for LRTIs? (Median 9 days in this study in the non PCT group- Table 3). How might this affect the results?

PCT is a great measure if used appropriately. Not every hospital is gifted to have a proactive micro team like SRH. Inappropriate and uncontrolled PCT use without proper micro input could lead to wastage of resources.

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hannah

12/11/2013 08:46:49 am

I agree with Emma that it is great to see PCT being used in this hospital, However this PCT trial seems very good for use in a ED setting as designed. The greatest groups with a saving in antibiotic prescription are those treated as outpatients or for minor acute bronchitis which is known to be commonly viral in origin. I suppose it gives the discharging doctor further evidence to back up a decision to treat as viral, which i know can be a tricky call to make.
For the ITU population this data I think is more difficult to extrapalate. CAP mostly got antibiotics in both groups, with similar outcomes.
ITU patients were allowed to go off protocol.
Patients in hospital for more than a few days were excluded and the decision making of stopping/starting antibiotics beyond the first 7 on ICU is a bigger challenge.

My other thought was that the study describes the inital negative test of PCT being repeated within 24 hours and the study i couldn't see whether it clarified the number of patients who recieved possibly delayed treatment by an initially normal PCT which later became elevated.

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L

17/11/2013 04:39:44 am

I appreciate the difficulty of conducting research & must complement the authors on this sizeable study looking at an important topic. But… I do have a few concerns/comments

Who should use this data?? As Hannah suggested front of house ED/MAU. Its relevance to ICU will be in de-escalation of antibiotics in those admitted hyper-acute respiratory failure where persistent low PCT are recorded within the first few days in keeping with clinical findings. I don’t think from this study we can extrapolate it to those already admitted to ICU.

Patient selection: I don’t think patients admitted from the community & those form nursing homes are comparable…as the later have a multitude of risk factors for multi-drug resistant bacterial infections (which would make treatment failure more likely) so I am not sure why they were included. Also I can’t see how many of this group were included in the study population.

But my major “beef” with this study is that the (in some cases paid) study clinicians evaluated adverse events in an unblinded fashion. Really…. you couldn’t find someone else to do this??? If not they could have utilised independent blinded clinicians to randomly select & review patients as a quality assurance measure.

To me the stats sound so complicated there could be a fudge…I will leave Deva to tease that out.

Ultimately the study highlights a recurring theme: There will always be patients in which protocols will be overruled which in my (possible controversial) opinion is acceptable – embracing why medicine should not be unquestioned protocolised care. Although in this study protocol adherence was over 75% which is much higher than that seen in other PCT studies I have read.

Bottom line: The study also shows that clinicians can safely & happily use PCT as an adjunct to support decision making when not starting antibiotics therapy in ED. But they feel less certain about how to use it when stopping antibiotic therapy.

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Deva

17/11/2013 09:09:26 am

To keep the discussion going...
We can use this paper to discuss 'managing missing values' for those that are interested in stats (we discussed it only last week so it is still fresh in my mind).

In a study, if you have some (or several) missing values, there are a few ways of handling those values / analysing data:

1. Remove the entire patient record from analysis - This will bring your sample size down and might potentially introduce a bias. (e.g. say you are comparing pain relief from epidural vs PCA for 'A' surgery. Lets say pain score is one of the variables you collect. If epidural patients are asleep and as a result pain scores are not collected (it becomes missing data), then it introduces a bias if you remove the entire record from analysis (epidural patients were asleep because they had better analgesia, but your analysis after removing them may not reflect this)

2. Ignore the missing value only for a particular analysis - This is only possible if you have values for other variables for the same patient. The problem here is the sample size differs for each variable (from prev example - no pain score value, but other data like type of local anaesthetic, level of epidural etc available)

3. Single imputation - The data set is arranged in a particular order and the missing value gets calculated from the previous available value in the list or gets calculated from other dependent values (for example predicting the haemoglobin based on the volume of blood lost)

4. Multiple imputation - Complex computer based algorithm calculates the value for us ( It is hard for me to understand this Rubin's technique - sorry).

5. If it is a binary outcome (alive or dead) then one can do a 'most conservative' (all patients that missed followup are dead) or 'least conservative' (all alive) so the readers can have their own 'judgemental happy medium' in between.

This study has used one of the multiple imputation methods to calculate the missing follow up data. Of course not having any data missing is the best, but it is not always possible and this method is the nearest best method.

If anyone knows and can explain the imputation techniques better please share it with us.

Thanks everyone for the thought provoking and interesting comments...keep them coming....

(Note: Please don't swear at me if you hate stats!. The idea is to discuss some stats with every paper, but the intensity and complexity varies with the paper. Remember in this process, we are learning too...!)

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L

18/11/2013 03:54:39 pm

Sorry Deva, there was no cursing of your name in the construction of the last post, rather admiration & envy of your understanding xx